Semi-Supervised Learning (SSL) approaches learn data distribution by jointly exploiting both labeled and unlabeled data. These techniques expand the training data by utilizing unlabeled samples along with labeled ones in order to construct a relationship between feature space and class labels. Several SSL-based HSIC frameworks have been proposed in the literature that can mainly be categorized as follows: i) Co-training, ii) Self-training, iii) Generative Adversarial Networks (GANs), iv) Graph-based SSL models and v) Semi-supervised SVM. A recent comprehensive survey on these SSL techniques can be found in [1]}. Moreover, another in-depth survey of SSL approaches is also presented in [2]}.
